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When Work Disappears: Manufacturing Decline and the Falling Marriage
Market Value of Young Men∗
David Autor†^ David Dorn‡^ Gordon Hanson§
April 2018
Abstract We exploit the gender-specific components of large-scale labor demand shocks stemming from rising international manufacturing competition to test how shifts in the relative economic stature of young men versus young women affected marriage, fertility and children’s living circumstances during 1990-2014. On average, trade shocks differentially reduce employment and earnings of young adult males. Consistent with Becker’s model of household specialization, shocks to male’s relative earnings reduce marriage and fertility. Consistent with prominent sociological accounts, these shocks heighten male idleness and premature mortality, and raise the share of mothers who are unwed and the share of children living in below-poverty, single-headed households. Keywords: Marriage Market, Fertility, Mortality, Household Structure, Single-Parent Fami- lies, Trade Flows, Import Competition, Local Labor Markets JEL Classifications: F16, J12, J13, J21, J
∗This paper previously circulated under the title “The Labor Market and the Marriage Market” (first circulating draft May 12, 2014). Autor, Dorn and Hanson acknowledge funding from the Russell Sage Foundation (RSF Project #85-12-07). Dorn acknowledges funding from the Spanish Ministry of Science and Innovation (grants CSD2006- 00016 and ECO2010-16726) and the Swiss National Science Foundation (grant BSSGI0-155804). Autor and Hanson acknowledge funding from the National Science Foundation (grant SES-1227334). We thank Andrew Cherlin, JanetCurrie, Marianne Page, Ann Huff Stevens, Kathleen Vohs, Jane Waldfogel, two anonymous referees, and numerous seminar and conference participants for valuable suggestions. We are grateful to Juliette Fournier, Ante Malenica, Timothy Simmons, Oscar Suen, Juliette Thibaud, and Melanie Wasserman for expert research assistance. † ‡MIT Department of Economics and NBER. E-mail: dautor@mit.edu §University of Zurich and CEPR. E-mail: david.dorn@econ.uzh.ch UC San Diego and NBER. E-mail: gohanson@ucsd.edu
“The consequences of high neighborhood joblessness are more devastating than those of high neighborhood poverty... Many of today’s problems in the inner-city ghettos—crime, family dissolution, welfare, low levels of social organization, and so on—are fundamen- tally a consequence of the disappearance of work.” William Julius Wilson, When Work Disappears, 1996, pp. xiii. “Wilson’s book spoke to me. I wanted to write him a letter and tell him that he had described my home perfectly. That it resonated so personally is odd, however, because he wasn’t writing about the hillbilly transplants from Appalachia—he was writing about black people in the inner cities.” J.D. Vance, Hillbilly Elegy: A Memoir of Family and Culture in Crisis, 2016, p. 144.
1 Introduction
An influential body of work associated with sociologist William Julius Wilson (1986; 1987; 1996) hypothesizes that the decline of U.S. blue-collar employment has diminished the pool of economically secure young adult men, thereby reducing women’s gains from marriage, eroding traditional parental roles, and imperiling children.^1 Wilson’s narrative is close relative of the classic Becker (1973) framework in which the economic gains to marriage arise in part from spousal earnings differences, which spur household specialization.^2 Reflecting the difficulty of distinguishing cause from effect in the correlations between labor-market opportunity and family structure, the literature has, with important exceptions, faced a challenge in testing these hypotheses.^3 We surmount this challenge by assessing how adverse labor-market shocks for young adults, emanating from rising trade pressure on U.S. manufacturing, affect marriage, fertility, and children’s living circumstances. Following Autor et al. (2013b) and Acemoglu et al. (2016), we exploit cross-industry and cross-local-labor- market variation in import competition stemming from China’s market reforms to trade to identify labor-demand shocks that are concentrated on manufacturing. In linking local-labor-demand shocks to marriage and fertility, our work is close in spirit to Black et al. (2003) who document an increasing prevalence of single-headed households in four U.S. states that suffered a decline in their coal and steel industries, and Kearney and Wilson (2017) who observe rising fertility but no change in marital patterns in U.S. regions that benefited from the 2000s fracking boom. Our study complements the evidence from these episodes of industry-specific booms (^1) See also Jahoda et al. (1971), Murray (2012), Bailey and DiPrete (2016), and Greenwood et al. (2017). (^2) Whereas Becker focuses on relative economic stature, Wilson’s argument further implies that holding gender differentials constant, an 3 absolute fall in male economic stature reduces the value of marriage. Exceptions include Angrist (2002) and Charles and Luoh (2010).
shrink the pool of economically secure young adult men and erode traditional household arrange- ments. Because trade-induced manufacturing shocks generate both an absolute fall in the employ- ment and earnings of young adult men and a fall in these outcomes relative to women, our empirical setting does not allow us to cleanly distinguish between the Becker hypothesis—focusing on relative economic stature—and Wilson’s thesis, focusing on men’s absolute economic stature. Alongside providing support for the argument that contracting blue-collar employment catalyzes changes in gender roles and household structures, our analysis indicates that Wilson’s conclusions apply to a far broader group of adults than the urban poor African Americans on whom he focused, and that the magnitude of these effects are sizable relative to observed declines in male employment rates, female fertility, and prevalence of marriage among U.S. young adults.^7
2 Empirical Approach
We examine changes in exposure to international trade for U.S. CZs associated with the growth in U.S. imports from China. Rising trade with China is responsible for nearly all of the expansion in U.S. imports from low-income countries since the early 1990s (Pierce and Schott, 2016a). Our empirical strategy builds on Autor et al. (2013a) and Acemoglu et al. (2016). We approximate local labor markets using the construct of CZs developed by Tolbert and Sizer (1996), and include the 722 CZs that cover the U.S. mainland. Our measure of the local-labor-market shock is the average change in Chinese import penetration in a CZ’s industries, weighted by each industry’s share in initial CZ employment:
∆IP cuiτ = ∑ j
Lij 90 Li 90 ∆IP^ jτcu.^ (1)
Here, ∆IP (^) jτcu = ∆M (^) jτcu /(Yj 91 + Mj 91 − Xj 91 ) is the growth of Chinese import penetration in the U.S. for industry j over period τ , which in our data include the time intervals 1990 to 2000 and 2000 to 2014. It is computed as the growth in U.S. imports from China, ∆M (^) jτcu , divided by initial absorption (U.S. industry shipments plus net imports, Yj 91 + Mj 91 − Xj 91 ) in the base year 1991, near the start of China’s export boom. The fraction Lij 90 /Li 90 is the share of industry j in CZ i’s total employment, as measured in County Business Patterns data in 1990. Differences in ∆IP cuiτ of parental job loss on children’s living circumstances. 7 Vance (2016) argues that Wilson’s observations now apply more broadly to the white working class. We confirm that our key findings hold when we focus on non-Hispanic whites.
across CZs stem from variation in local industry employment structure in 1990, which arises from differential concentration of employment in manufacturing versus non-manufacturing activities and specialization in import-intensive industries within local manufacturing. In all specifications, we control for the start-of-period manufacturing share within CZs so as to focus on variation in exposure to trade stemming from differences in industry mix within local manufacturing. The measure ∆IP cuiτ captures overall trade exposure experienced by CZs but does not distinguish between employment shocks that differentially affect male and female workers. To add this dimension of variation, we modify (1) to exploit the fact that manufacturing industries differ in their male and female employment intensity—so that trade shocks of a given magnitude will differentially affect male or female employment depending on the set of industries that are exposed. We incorporate this variation by multiplying the CZ-by-industry employment measure in (1) by the initial period female or male share of employment in each industry by CZ (fij 90 and 1 − fij 90 ), thus apportioning the total CZ-level measure into two additive subcomponents, ∆IP m,cuiτ and ∆IP f,cuiτ :
∆IP m,cuiτ = ∑ j
(1 − fij 90 ) Lij 90 Li 90 ∆IP^ jτcu and^ ∆IP^ f,cuiτ =^ ∑ j
fij 90 Lij 90 Li 90 ∆IP^ jτcu.^ (2)
As shown in Appendix Table A1, Chinese import penetration rose by 0.95 percentage points between 1990 - 2000, with an additional 1.15 percent rise per decade over 2000 - 2014. Sixty percent of this rise accrued to male employment.^8 We identify the supply-driven component of Chinese imports by instrumenting for growth in Chinese imports to the U.S. using the contemporaneous composition and growth of Chinese imports in eight other developed countries.^9 Our instrument for the measured import-exposure variable ∆IP cuit is a non-U.S. exposure variable ∆IP coit that is constructed using data on industry-level growth of Chinese exports to other high-income markets:
∆IP coiτ = ∑ j
Lij 80 Li 80 ∆IP^ jτco.^ (3)
This expression differs from (1) by using realized imports from China by other high-income markets (^8) We construct (1) using trade data from UN Comtrade that we harmonize to 4-digit SIC industries, and data on CZ employment by industry from the County Buisness Patterns. In (2), we further use Census IPUMS data to compute gender shares within industries and CZs, and assign to each SIC industry in a CZ the gender share of theCensus industry in the CZ encompassing it. Most outcome variables are based on Census IPUMS samples for 1990 and 2000 (Ruggles et al., 2004) and pooled American Community Survey samples for 2013 through 2015. We allocate PUMAs to CZs using the algorithm in Dorn (2009) and Autor and Dorn (2013). 9 The eight comparison countries—determined by the availability of comparable trade data for the full sample period—are Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain, and Switzerland.
Table 1: Estimated Impact of Manufacturing Trade Shock on Manufacturing Employ- ment by Gender and Gender Differential in Employment Status, Earnings, and Idleness, 1990-2014: 2SLS Estimates. Dependent Variables: Changes in Percentage of Population Age 18-39 that is Employed in Manufacturing, Changes in Gender Differentials in Employ- ment Status (in % pts); Change in Gender Differential in Annual Earnings (in $); Change in Gender Differential in Percentage of Young Adults Age 18-25 that is Employed, Not Employed but in School, or Neither Employed nor in School
M+F Males Females Emp Unemp NILF (1) (2) (3) (1) (2) (3) -1.06 ******^ -0.99 **^ -1.09 **^ -0.65 *^ 0.19 *^ 0.46 ~ (0.17) (0.17) (0.20) (0.26) (0.09) (0.24) -1.21 **^ -2.59 **^ 0.20 -3.13 **^ 0.38 2.75 ** (0.44) (0.51) (0.43) (0.78) (0.26) (0.62) -0.88 *^ 0.82 ~^ -2.56 **^ 2.17 **^ -0.02 -2.15 ** (0.35) (0.46) (0.38) (0.65) (0.26) (0.64) Mean Outcome Variable -2.61 -3.19 -2.06 -2.74 0.03 2. Level in 1990 12.98 17.37 8.68 14.64 1.22 -15. No Emp No Emp P25 Median P75 Emp In School No School (1) (2) (3) (1) (2) (3) -672 ** -445 * -847 * -0.64 ~^ -0.02 0.66 ** (193) (191) (334) (0.34) (0.26) (0.20) -2,216 ** -2,945 ** -3,685 ** -3.16 **^ 0.56 2.60 ** (516) (593) (1081) (1.03) (0.73) (0.60) 1,086 * 2,400 ** 2,384 ** 2.24 *^ -0.68 -1.55 ** (529) (630) (814) (0.92) (0.74) (0.56) Mean Outcome Variable -1,894 -2,126 -2,491 -2.83 -0.25 3. Level in 1990 6,926 13,376 17,489 7.70 0.87 -8. Notes: N=1444 (722 CZ x 2 time periods). Panel C analyzes the change over time in the difference between a percentile of the unconditional male earnings distribution in a commuting zone and the corresponding percentile in the unconditional female earnings distribution. All models include a dummy for the 2000-2014 period, occupational composition controls (start-of-period indices of employment in routine occupations and of employment in offshorable occupations as defined in Autor and Dorn, 2013), start-of-period shares of commuting zone population that is Hispanic, black, Asian, other race, foreign born, and college educated, as well as the fraction of women who are employed. Models are weighted by the product of period length and commuting zone share of start-of-period U.S. mainland population. Robust standard errors in parentheses are clustered on state. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.
Δ Import Penetration II. Male Industry vs Female Industry Shock Δ Import Penetration × (Male Ind Emp Share) Δ Import Penetration × (Female Ind Emp Share)
C. Male-Female Differential in Annual Earnings ($), Age 18-
D. M-F Diff in Idleness, Age 18-
I. Overall Trade Shock Δ Import Penetration II. Male Industry vs Female Industry Shock Δ Import Penetration × (Male Ind Emp Share) Δ Import Penetration × (Female Ind Emp Share)
I. Overall Trade Shock
A. Manufacturing Employment as a Share of Population, Age 18-
B. Male-Female Differential by Employment Status Age, 18-
Figure 1: Impact of Manufacturing Trade Shock on Earnings of Males and Females Age 18-39, 1990-
0
Dollars (2015)
(^0 10 20 30) Percentile of Income Distribution 40 50 60 70 80 90 100 Male Earnings Female Earnings A. Impact on Male and Female Annual Earnings by Percentile, 1990-
-^ -^ -^ -^ -^ -^ -^
0
Percentage of Male Earnings
(^0 10 20 30) Percentile of Income Distribution 40 50 60 70 80 90 100 B. Impact on Male-Female Annual Earnings Gap 1990- as a Percentage of 1990 Male Earnings
The top panel measures the impact of a unit trade shock on the unconditional distribution of annual earnings (in $2015) separately for males and females.Each dot indicates a coefficient estimate from a separate IV quantile regres- sion with group-level treatment (Chetverikov, Larsen and Palmer 2016) that controls for the covariates indicated in Table 1, and shaded areas indicate a 95% confidence interval. The bottom panel reports the effect of a unit trade shock on the difference in the male-female annual earnings gap expressed as a percentage of male earnings in 1990 at the indicated percentile.
lockstep—underscore that differential manufacturing exposure is not the explanation. Rather, these estimates indicate that trade shocks differentially reduce male employment in non-manufacturing. Panel A of Appendix Table A3 indicates that the overall employment loss of males due to a unit trade shock is larger than the decline in manufacturing employment seen in panel A of Table 1 (− 1. 5 vs − 1. 0 percentage points) while the overall employment decline for women is slightly smaller than in manufacturing (− 0. 9 vs − 1. 1 percentage points).^16 We next quantify the impact of gender-specific trade shocks on the distribution of annual wage and salary income. For this analysis, we implement the Chetverikov et al. (2016) technique for per- forming instrumental-variable estimates of the distributional effects of group-level treatments. Panel C of Table 1 shows estimates of the effect of trade shocks on the CZ-level male-female earnings gap for the 25 th, 50 th, and 75 th^ percentiles of the distribution. Within CZs, male earnings substantially exceed female earnings at all quantiles, with the size of the gap rising steeply with the quantile index. In 1990, this gap was $6, 926 , $13, 376 , and $17, 489 at the 25 th, 50 th, and 75 th^ quantiles, respectively (bottom rows of panel C). Between 1990 and 2014, these gaps compressed by $1, 894 , $2, 126 and $2, 491 per decade at the 25 th, 50 th, and 75 th^ quantiles respectively. Reinforcing the panel B findings for the gender gap in employment, the first row of estimates in panel C demonstrates that trade shocks differentially curtail male earnings. A one-unit trade shock reduces male relative to female earnings by $672 at the 25 th^ percentile (column C1, t = − 3. 5 ), by $445 at the median (column C2, t = − 2. 3 ), and by $847 at the 75 th^ percentile (column C3, t = − 2. 5 ).^17 Since the male-female earnings gap is smaller at lower wage quantiles, the relative impact of trade shocks on the male-female wage gap is largest among low-earners, as seen in Figure 1. The first panel details that trade-induced earnings losses are larger for males than females at every quantile from the 15 th^ to 95 th^ percentile.^18 The second panel reports the impact of a unit trade shock on the male-female annual earnings gap expressed as a percentage of baseline male earnings in 1990 at the corresponding percentile. Trade shocks modestly compress the male-female annual earnings gap in the upper half of the annual earnings distribution. The effect is more dramatic below: the (^16) Acemoglu et al. (2016) find substantial employment losses in industries that sell their outputs to import-competing manufacturing, including mining whose workforce is strongly male-dominated. (^17) Panel C-II of Table 1 shows that shocks centered on male employment have a larger effect on the gender earnings gap than do shocks centered on female employment. 18 Annual earnings for both genders are zero below the 10 th^ percentile. Above the 95 th^ percentile, earnings are largely censored and then imputed by the Census Bureau.
male-female wage compression is 2 points at the median, 4 points at p 35 , and 6 points at p 20.^19 The gender-specific estimates in Appendix Table A3 confirm that trade shocks reduce employ- ment and earnings of both genders; that employment and absolute earnings losses are larger for males than for females; and that proportional earnings losses for both sexes are larger at low than high percentiles. These findings support Wilson’s observation that manufacturing contractions shrink the pool of economically secure young adult men, generating both an absolute fall in the employment and earnings of young adult men and a fall in these outcomes relative to women.
3.2 Gender gaps in idleness, absence, and mortality
The heart of the Wilson hypothesis is that adverse shocks to blue-collar employment catalyze a broader deterioration in adult social function. We test for such consequences with three non-market measures: idleness, absence, and mortality. Idleness is the state of being neither employed nor in school; we focus on the ages 18-25, which cover the transition between school and work.^20 In panel D of Table 1, we estimate a variant of (4) where the dependent variable is the male-female gap in three main (mutually exclusive) activity statutes: currently employed (D1), not employed but enrolled in school (D2), and neither employed nor enrolled in school (D3), which we refer to as idleness. Column D1 shows that a unit trade shock lowers the fraction of young men employed by 0. 64 percentage points relative to women of the same age range (t = − 2. 5 ). This is nearly identical to the effect found for the broader set of adults ages 18-39 considered in column B1. Column D3 finds that the entire differential rise in non-participation among young males is due to increased idleness ( 0. 66 points, t = − 2. 5 ), with little effect on school enrollment (column D2). This pattern is reinforced when focusing on the gender-specific components of trade exposure (panel D-II): shocks to male-intensive manufacturing generate a larger differential increase in male idleness ( 2. 6 points, t = 4. 3 ) than do shocks to female-intensive manufacturing (− 1. 6 points, t = − 2. 8 ).^21 Panel C of Appendix Table A reports these impacts separately by gender. The differential effect of manufacturing shocks on the male-female idleness gap stem entirely from increases in male idleness. By contrast, reductions in (^19) These reductions are relative to the baseline male earnings level not the baseline gap, indicating large changes. We truncate the estimates at 20 p 20 because the low values of the denominator below this point make estimates uninformative. Aguiar, Bils, Charles and Hurst (2017) document that young men devote more time to video games and recre- ational computer use, while working fewer hours. 21 In panel D, unemployed adults are categorized as either students or as idle. If we define idleness as the state of being neither employed, unemployed, nor in school, we continue to find a significant differential impact of trade shocks on male idleness. The corresponding point estimates and standard errors for column D3 under this definition are: 0 .34 (0.15), 2 .13 (0.41), and − 1 .69 (0.43) for rows 1, 2, and 3 respectively.
well-measured, has an unambiguous interpretation, and has attracted attention following Case and Deaton (2015; 2017). Using U.S. Vital Statistics files enumerating person-level death certificates for all U.S. residents, Table 2 reports the impact of trade shocks on the gender gap in cumulative mortality per decade—overall and by cause—per 100 K adults ages 20-39.^23 Our analysis is related to Pierce and Schott (2016b), who link county-level trade exposure to rising mortality due to acci- dental poisoning and suicide in the working-age population. Guided by our focus on the interaction between labor markets and marriage markets, our analysis focuses on mortality among young adults ages 20-39 and on differential effects on males versus females. Shocks to import penetration significantly increase the male-female mortality gap among young adults. The point estimate in column B3 of the upper panel of Table 2 indicates that a unit trade shock induces an additional 69. 6 male relative to female deaths per 100 K adults (of each gender) per decade. Given an average differential mortality rate of 1200 per 100 K adults per decade over 1990 - 2015, this increment is large. Subsequent columns decompose the overall mortality effect into by-cause categories using the scheme in Case and Deaton (2015, Figure 2). Case and Deaton (2015; 2017) show that drug and alcohol (D&A) related mortality rose by epidemic proportions among working-age adults in this time period. The bottom of column B4 indicates that D&A deaths accounted for 10 percent of all young adult male deaths between 1990 - 2015, while the point estimate in the upper row of the column demonstrates that the male-female gap in D&A deaths surged in trade-impacted CZs. The point estimate of 21. 9 (t = 3. 1 ) accounts for one-third of the total contribution of trade shocks to differential mortality. Columns B5 through B8 test for corresponding trade shock-related increases in differential male mortality from liver disease (often alcohol-related), diabetes, lung cancer, and suicide. No effect is significant. The final column (B9) combines all other causes of death beyond those emphasized by Case and Deaton (2015), including infectious diseases, neoplasms and accidents, which account for three of every four young-adult deaths. The point estimate of 41. 2 indicates that trade shocks Pierce and Schott (2016b) document statistically significant increases in crime incidents and arrests in such CZs during the 1990s and 2000s. Because incarceration and homelessness are disproportionately prevalent among males (West and Sabol 2008, Table 1 and Appendix Table 7; U.S. Conference of Mayors, 2007, Exhibits 2.3 and 2.4), a rise in either may reduce the number of males enumerated in the non-institutional population. 23 These vital statistics data, used under agreement with the U.S. Center for Disease Control, cover deaths occurring in 1990 through 2015. The corresponding birth data (used below) extend through the year 2016. The denominator for death rates is the CZ-level population reported by the Census Bureau, which is available for the age bracket 20-39. The dependent variable is normalized to correspond to a 10 -year cumulative value. Our regression models include the vector of start-of-period control variables used in previous tables and account for serial correlation in CZ-level mortality rates by additionally controlling for lagged cumulative decadal mortality.
contribute to an overall increase in the gender mortality gap among all remaining causes, with this estimate marginally significant (t = 1.8).^24 In net, the differential increase in male mortality can account for a 16 percent of the fall in the fraction of males among young adults in trade-impacted CZs (column A1 of Table 2).^25 While only a small minority of adults who engage in risky behaviors experience fatal consequences, the remainder may be less attractive marital partners due to their substance abuse. Thus, the differential rise in drug and alcohol abuse among young adult males in trade-exposed locations may imply a fall in the marriage-market value of a broader set of young males.^26
3.3 Fertility, marriage, and children’s living circumstances.
We test finally for impacts of trade shocks on fertility, marriage, and children’s circumstances. Panel A of Table 3 presents the impact of trade exposure on marital status among women ages 18-39, whom we classify as currently married, currently widowed, divorced or separated, or never-married.^27 Trade shocks deter marriage formation: a one-unit trade shock predicts a 0. 95 percentage-point decline in the the fraction of young women who are currently married (column A1, t = − 3 .1), a further
- 21 point decline (column A2, t = − 2. 0 ) in the fraction of women who are previously married, and a corresponding rise of 1. 2 points in the fraction of women never married (column A3, t = 3.5). Shocks to male and female-intensive employment have opposing and precisely estimated effects on marriage formation (columns A1-II through A3-II): a one unit shock to male-intensive employment reduces the fraction of young adult women ever married by 4. 2 points (t = 6. 6 , a 12 percent rise on a 1990 base of 34. 8 percent) and the fraction currently married by 3. 6 points (t = − 5. 8 ); a unit shock to female-intensive employment has a countervailing impact on marital status that is about two-thirds as large as the impact of a shock to male-intensive employment. We find corresponding results for fertility, measured as births per 1 , 000 women ages 20-39. (^24) Models using gender-specific trade shocks (panel B-II) find that essentially all of the gender-differential in mortality effects stems from shocks to male-intensive employment. Similarly, models for by-gender mortality, which are reported in Appendix Table A4, find that the mortality response to trade shocks stems almost entirely from male deaths. 25 A unit trade shock reduces the male fraction of population by 0.25 per 100 adults among those ages 18-39 over the course of a decade (Table 2), implying an effect of 500 per 100K men. A unit trade shock raises excess male versus female mortality by 70 for every 100 K adults of each sex among those ages 20-39 over the course of a decade (Table 2). Adjusting for the wider age range of the population versus mortality bracket ( 22 versus 20 years), this number rises to 77 per 100 K adults. Thus, excess mortality can account for a share of 77 /500 = 0. 16 of the decline in the male share of the young adult population in trade-impacted CZs. 26 27 See also Charles and Luoh (2010) and Caucutt et al. (2016). If a woman is currently married, we cannot determine if she was previously widowed, divorced or separated.
Table 3: Estimated Impact of Manufacturing Trade Shock on Marriage, Fertility, Maternal Status, Childhood Poverty, and Household Structures of Adult Women and Dependent Children, 1990-2014: 2SLS Estimates. Dependent Variables: Changes in Women’s Marital Status, Births per 1,000 Women, Fraction of Women with Children, and Fraction of Mothers Unmarried; Fraction of Children Living in Poverty; and Household Type of Women and Children
(5) -0.95 **^ -0.21 *^ 1.16 **^ -1.94 **^ -0.66 **^ 0.52 ~^ 0.61 * (0.30) (0.11) (0.33) (0.54) (0.23) (0.31) (0.26)
-3.57 **^ -0.66 **^ 4.23 **^ -5.45 **^ -1.79 **^ 3.28 **^ 2.13 ** (0.62) (0.22) (0.64) (1.19) (0.63) (0.73) (0.70) 2.03 **^ 0.29 -2.32 **^ 2.06 ~^ 0.62 -2.62 **^ -1. (0.55) (0.19) (0.58) (1.16) (0.52) (0.85) (0.82) Mean Outcome Var -6.92 -1.62 8.55 -2.04 -3.53 6.56 1. Level in 1990 53.05 12.11 34.84 86.87 53.24 23.98 17.
(5) -0.81 **^ -0.22 ~^ 1.03 **^ -0.35 ~^ -0.11 0.30 **^ 0. (0.27) (0.12) (0.30) (0.19) (0.07) (0.11) (0.16)
-3.21 **^ 0.04 3.17 **^ -1.85 **^ 0.28 1.43 **^ 0. (0.55) (0.28) (0.60) (0.50) (0.23) (0.32) (0.42) 1.93 **^ -0.52 **^ -1.41 **^ 1.36 *^ -0.55 *^ -0.98 *^ 0. (0.54) (0.20) (0.52) (0.55) (0.25) (0.42) (0.29) Mean Outcome Var -7.57 1.65 5.93 -4.69 1.62 1.79 1. Level in 1990 50.30 5.25 44.45 71.39 2.82 16.82 8. Poverty Rate (%) 1990 n/a n/a n/a 8.7% 42.3% 47.4% 28.8%
Births per 1, Women
Widowed Divorced Separated
Never Married
B. Fertility and Maternity
Δ Import Penetration
Δ Import Penetration × (Male Share) Δ Import Penetration × (Female Share)
II. Male vs Female Industry Shock
(1) (2) (3) (4)
(7)
Married Couple
Parent + Unmarried Partner
Single Parent, No Partner
Grand- parent or Other (1) (2) (3) (4) (6) I. Overall Trade Shock Δ Import Penetration
Δ Import Penetration × (Male Share) Δ Import Penetration × (Female Share)
Notes: N=1444 (722 CZ x 2 time periods). Outcomes in panels A, B and D consider adult women ages 18-39 while those in panels C and E consider children ages 0-17. Fertility in column B4 is measured through 2016 while all other outcomes are measured through 2014. Dependent variables are: fraction of women with any biological, adopted, or stepchildren in the household (B5); fraction currently married among women with children in the household (B6); the fraction of children in households below the official Census poverty line (C7). Columns D1 and D2 refer to households where either (1) the woman is the spouse or partner of the household head or (2) she is the household head and has a spouse or partner who is living in the household. Column D3 comprises all other household structures. Dependent variables in columns E4-E7 are the fraction of children in each household type: household head is a married parent of the child (E4); household head is a parent with cohabiting partner (E5); household head is a single parent (E6); or household head is a grandparent, other relative, or non-related caregiver (E7). All regressions include the full set of control variables from Table 1, are weighted by the product of period length and CZ population share, and standard errors are clustered on state. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.
II. Male vs Female Industry Shock
D. Women's Household Type Living w/ Spouse
Living w/ Partner
Other HH Structure
A. Women's Marital Status
Married I. Overall Trade Shock
% of Women w/ Children
% Mothers Unmarried (6) (7)
C. % of Children in HH < Poverty Line
E. Children's Household Type
Columns B5, B6, and C7 of Table 3 affirm Wilson’s prediction. Column B5 shows that a unit trade shock reduces by 0. 66 points the fraction of adult women ages 18-39 with children in the household (t = − 2. 9 ). Because this effect is only half as large as the increase in the fraction of women ages 18-39 who are never-married (column A3), the shock raises the share of mothers who are unmarried (column B6, βˆ = 0. 52 , t = 1. 70 ), while the share of children living in poverty also increases (column C7, βˆ = 0. 61 , t = 2.3). Disaggregating the trade shock into its gender-specific components (columns B5-II, B6-II, and C7-II), trade shocks to male employment reduce the fraction of women with children (by 1. 8 points) while raising the share of mothers who are unmarried by 3. 3 points (t = 4. 5 ) and the share of children living in poverty by 2. 1 points (t = 3. 0 ); shocks to female employment raise the prevalence of motherhood, reduce the fraction of mothers who are unmarried, and reduce the fraction of children living in poverty. Panels D and E complete the picture of household adjustment by considering women’s and children’s living circumstances. Consistent with the panel A findings for marriage, a unit trade shock reduces the fraction of women living with a married partner by 0. 81 percentage points and the fraction cohabiting with an unmarried partner by additional 0. 22 points. The declining marriage rate is thus not compensated by a rising propensity of young unmarried women to live with a partner. Panel E documents how these countervailing effects on fertility, marriage, and single motherhood net out for children’s circumstances. In column E4, the fraction of children living in married two- parent households falls by 0. 35 points per unit trade shock (t = − 1. 7 ), while the fraction living in single-parent, non-cohabiting households rises by 0. 30 points (column E6, t = 2. 8 ). Echoing our findings for marriage and fertility, in panel E-II these adverse effects on children run entirely through shocks to male employment, which raise the share of children living in single-headed, non-cohabiting couples. Adverse shocks to female employment have protective effects for children, significantly raising the share of children in married households, reducing the share in non-married cohabiting and single-headed households, and weakly reducing the fraction of children living in poverty.
4 Conclusions
Our analysis confirms William Julius Wilson’s hypothesis that contracting blue-collar employment catalyzes changes in marriage, fertility, household structures, and children’s living circumstances.
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